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A practical requirement that shows up immediately is secure, explainable access management. Agents should only see what they need, every access should be attributable, and high-risk actions should be inspectable and reversible.
Agents become useful when they can do work in your ecosystem: read from data products, call APIs, trigger workflows, and write results back into your systems. This step explains how to add that capability on top of an existing data platform without creating a parallel “AI platform” nobody can operate.
Transitioning from a traditional data platform to an AI platform capable of supporting agents is an evolutionary step, not a restart. The foundations built over years—storage, ingestion, ETLs, metadata, governance—remain essential. Agentic AI adds new requirements: autonomy, semantics, and latency.
Define what the agent should do from start to finish and what qualifies as success. Similar to guiding a junior employee, agents need clarity on where work begins, where it ends, and what “done” means.
Agents must execute work in controllable, transparent segments. Support modular, reusable workflows, so tasks can be broken into smaller parts, observed, and rolled back when needed.
Agents must remember what they have done. With proper state management, agents resume from the last successful step instead of restarting after failures.
Provide short-term and long-term memory, enabling agents to adapt based on recent and historical context.
Building interfaces is one of the hardest steps. Do not postpone it. Tooling must enable agents to interact with enterprise systems via standardized, secure connections.
Support guardrails, evaluation frameworks, stress tests, and behavioral testing so teams can scale adoption safely.